Labeling data is expensive, whilst unlabeled data is often abundant and cheap to collect. Semi-supervised learning algorithms that can use both types of data can perform significantly better than supervised algorithms that use labeled data alone. However, for such gains to be observed, the amount of unlabeled data trained on should be relatively large. Therefore, making semi-supervised algorithms scalable is paramount. In this work we discuss several recent techniques for improving the scaling ability of these algorithms.